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Conover: AI models outclass human working memory, not core reasoning
“It's not clear that you get superhuman reasoning capabilities from human level demonstrations of skill. And by that, I mean the pre-training corpus, but then additionally, the fine tuning corpuses, I think you largely mimic the demonstrations that are present …”
Conover: Million-token context windows fail to extract deep insights from SEC filings
“It, I think empirically is not the case that you can just throw all of the SEC filings in, you know, a million token context window and get deep insight that is useful out the other end.”
Conover: Push-button automated financial modeling misses the core purpose of modeling
“The other piece of this is that the financial modeling is often very, when we talk to our users, it's very personal. So they have a specific view of how a company is structured. They have the, you know, one key driver of asset performance that they think is re…”
Conover: Investment thesis idea generation is absolutely automatable with AI
“I think that process of idea generation is absolutely automatable.”
Conover: AI model developers are absolutely overfitting to public evaluation benchmarks
“And I think the work around over, you know, overfitting on the test, I think is like that. 100% is happening.”
Conover: Economic incentives to pre-train commodity foundation models from scratch are diminishing
“The incentives, the economic incentives for companies to train their own foundation models, I think, are diminishing. So the, like, window in which you are the dominant pre-train, and let's say that you spend five to forty million dollars, you know, for like a…”
Conover: Identifying mispriced assets is ill-suited to human intellect
“If you think of the job of an active asset manager, the work to be done is to understand something about the market that nobody else has seen in order to identify a mispriced asset. And it's our view that that is not a task that is well suited to human intelle…”
Conover: Unbounded AI agents are useless compared to finite state machines
“Specifically, like, I don't think that unbounded agentic behaviors are useful and that instead a useful LLM system is more like a finite state machine where the behavior of the system is occupying one of many different behavioral regimes and making decisions a…”
Conover: The next generation of AI innovation requires specialized tuning data
“And I think the cost of producing instruction tuning and fine tuning data that creates specific kinds of behaviors, I think that's probably where the next generation of really interesting work starts to happen.”
Conover: Labor flow networks predict next-quarter S&P 500 market cap changes
“We demonstrated that five hundred million jobs transitions can be hierarchically clustered as a network of labor flows and in our predictive next quarter S and P 500 market gap changes.”
Conover: AI companies hire systems engineers, traditional software hires AI talent
“All of the traditional software companies are trying to hire AI talent and all the AI companies are trying to hire systems engineers, and that is 100% the case.”
Conover: Financial AI requires deep domain expertise for non-consensus insights
“Grammarly is a good example of a company that has Generative work product that is valuable by most humans. Whereas in finance, the character of the insight, the depth of insight and the non-consensusness of the insight really requires fairly deep domain expert…”
Conover: Commercial LLMs struggle to generate 5,000 output tokens in one generation
“There is a characteristic output length for these models. Let's say it's about 1200 tokens. Like it is very difficult to get any of the commercial LMs or LLAMA to write 5000 tokens.”
Conover: One-year patient adherence rate for Ozempic is only 35%
“Or for example, that adherence rates to a Zenpec after a year, just 35%.”
Conover: AI personalization must rely on revealed rather than stated preferences
“Getting a person to articulate everything that they believe is not a realistic task. Netflix doesn't ask you to describe what kinds of movies you like and they give you the option to vote, but nobody does this. And so what I think you do is you observe people'…”
Brightwave uses LLM supervision alongside human annotation benchmarks for model evaluation
“We pay human annotators to evaluate the quality of the generative outputs, and I think that that is always the reference standard, but we frequently first turn to LLM supervision as a way to
Have whether it's at fine tuning time or even for subsystems that ar…”
Conover: Repeatable AI annotation pipelines require unsexy people management
“It's one thing to do like a single monolithic push to create a,
Training data set like that, or an evaluation corpus, but I think it's another to have a repeatable process, and a lot of that, I think, realistically is pretty unsexy, like, people management wor…”
Conover: Nearest-neighbor search without metadata yields convincingly wrong AI answers
“If I just look for something that is a nearest neighbor without any of that temporal or other qualitative metadata overlay, you're just going to get a kind of a bag of facts. And that, that is like explicitly not helpful. Because the worst
Failure state for th…”
Conover: Programming LLMs as zero-marginal-cost machine learning systems is underappreciated
“I think that it is underappreciated how powerful, there's the generative capabilities of language models, but there's also the ability to program them to function as arbitrary machine learning systems, basically for marginally zero cost.”
Conover: LLMs enable knowledge graphs more granular than Bloomberg or LinkedIn
“We believe that there's an opportunity to create a knowledge graph that has resolution that greatly exceeds what any You know, whether it's Bloomberg or LinkedIn currently has access to where we're getting as granular as person X submitted congressional testim…”
Conover: LLMs do not need anthropomorphic personas for high-quality reasoning
“Our experience has been that You can get really, really high quality reasoning from roughly an agentic system without needing to be too cute about it. You can describe the task and you know, within well-defined bounds you don't need to treat the LLM like a per…”
Conover: Classical ML provides statistical output guarantees that LLMs cannot offer
“Traditional machine learning has a real material role to play in producing a system that hangs together, and there are, you know, guaranteeable Like statistical promises that classical machine learning systems to include traditional deep learning can make abou…”
Conover: Building automated Excel spreadsheets is an explicit non-goal for Brightwave
“I think what is an explicit non-goal for the company is to create Excel spreadsheets.”
Conover: Systematic hedge fund trading desks operate like large ML teams
“The more that I have learned about How teams at hedge funds actually behave, and you look at, like, systematics desks, or semi-systematic trading groups, man, it's a lot like a big machine learning team.”
Conover: AI models can parse documents to identify second-order derivative bets
“It is very straightforward to take a model and say, parse through all of these documents and find second order derivative bets and say, oh, it turns out that energy is like very, very adjacent to investments in AI and may not be priced in the same way that GPU…”
Conover: Databricks' Dolly model was never state-of-the-art, but proved a concept
“DALI was never the best open source model, but it demonstrated a new, Something that was not obvious to many people at the time. Yeah, but we always were clear that it was never state of the art.”
Brightwave raises $6M Seed round led by Decibel Partners
“We, we've raised a six million dollar seed round including participate lead by decibel we love working with and including Participation from .7 to two, one of the largest hedge funds in the world and moon fire ventures.”
Conover: A $20B crossover hedge fund uses Brightwave for equity research
“We have a twenty billion dollar crossover hedge fund and their equities team uses this tool to go deep on a thesis.”
Brightwave trains custom models to verify outputs against primary source material
“We train our own models to assess you know, you can think of this like entailment, like is, is this supported by the underlying primary sources?”
Conover: The most alpha-generating information in finance is often private
“Frequently the most interesting and alpha generating material is not publicly available”
Brightwave uses composable, context-aware prompting conditioned on retrieved context semantics
“You can have prompts that are composable and that have different sort of command units that like may or may not be present based on the semantics of the content that is being populated into the rag context window. And so that, that's something we make great us…”